Data centers house information technology (IT) equipment and other objects, such as servers, telecommunication systems and storage systems. This IT equipment is normally housed in racks. Data centers also contain secondary equipment to keep the IT equipment functioning, and, moreover, functioning under acceptable environmental conditions, such as acceptable ranges of heat and humidity. This secondary equipment is often referred to as “facilities equipment” and includes power distribution units (PDUs), computer room air conditioners (CRACs) and uninterruptable power supplies. It is often challenging to locate and classify the equipment in a large computing facility or data center.
When a computer data center is built or goes into service, there is often an accompanying layout document or blueprint that identifies the location and type of each piece of equipment in the center. This document is often created using a Computer Aided Design (CAD) tool or similar software. The layout shows the initial location of equipment at the time the data center was established and is typically updated only after a major redesign of the center. Such drawings are rarely updated as equipment is replaced and any of a myriad of minor layout changes are made to the center. Maintaining such a document is cumbersome and is therefore often neglected.
A number of techniques have been proposed or suggested for employing one or more robots to automatically navigate, map and monitor data centers. For example, J. Lenchner et al., “Towards Data Center Self-Diagnosis Using a Mobile Robot,” ACM Int'l Conf. on Autonomic Computing (ICAC '11) (2011), incorporated by reference herein, discloses a robot that serves as a physical autonomic element to automatically navigate, map and monitor data centers. The disclosed robot navigates a data center, mapping its layout, and monitoring its temperature and other quantities of interest with little, if any, human assistance. In addition, U.S. patent application Ser. No. 12/892,532, filed Sep. 28, 2010, entitled “Detecting Energy and Environmental Leaks in Indoor Environments Using a Mobile Robot,” incorporated by reference herein, discloses techniques for energy and environmental leak detection in an indoor environment using one or more mobile robots.
While the use of robots has greatly improved the ability to automatically monitor indoor environments, they suffer from a number of limitations, which if overcome, could further extend the utility and efficiency of robots that are monitoring an indoor environment. For example, the robots are unable to automatically classify the type of data center equipment.
A number of tagging techniques have been proposed or suggested to automatically classify the location and/or type of data center equipment. For example, barcodes and RFID tags have been used to allow equipment to be automatically identified. Barcodes and RFID tags, however, require that the equipment be previously tagged with a barcode and/or an RFID tag—a process that is labor intensive and hence expensive.
A need remains for automated techniques for identifying, locating and/or classifying objects, such as equipment in a data center or another environment. Yet another need remains for automated techniques for identifying, locating and/or classifying objects without having to tag the equipment in some way.